A language model determines a probability for a sequence of input items, such as a sequence of input words, letters, phonemes, etc. In some cases, the language model is agnostic with respect to the context in which it is applied at runtime. As such, users who input the same information into a language model in different contexts will receive the same output result. In other cases, language models have been built which attempt to take context into account at runtime, to varying extents. For example, a system may produce plural models for different topics based on different topic-centric training sets. The system then selects the most appropriate model at runtime for application to a particular instance of input information.
Known context-based language models have proven effective, yet there is room for improvement in this technological field.